1 citations · 2 across the 12 of their papers we have counts for
7 papers · 1 filter
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Fair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment
Woojun Kim, Katia Sycara
Multi-agent reinforcement learning in mixed-motive settings presents a fundamental challenge: agents must balance individual interests with collective goals, which are neither full…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…
CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation
Joonkyung Kim, Joonyeol Sim, Woojun Kim +2
We propose CARE (Collision Avoidance via Repulsive Estimation) to improve the robustness of learning-based visual navigation methods. Recently, visual navigation models, particular…
Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources
Lingpeng Chen, Siva Kailas, Srujan Deolasee +3
We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the nu…
Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
Muhan Lin, Shuyang Shi, Yue Guo +7
Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-…
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning
Woojun Kim, Katia Sycara
Overestimation arising from selecting unseen actions during policy evaluation is a major challenge in offline reinforcement learning (RL). A minimalist approach in the single-agent…